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from fastai.vision.all import *
import gradio as gr
import random
__all__ = ['is_rock', 'learn', 'classify_image', 'determine_winner', 'play game' 'categories', 'image', 'label', 'examples', 'intf']
def is_rock(x):
return x[0].issuper()
learn = load_learner('RPS_model2.pkl')
categories = ('paper', 'rock', 'scissors')
def classify_image(img):
pred, idx, probs = learn.predict(img)
return dict(zip(categories, map(float, probs)))
def determine_winner(user_choice, computer_choice):
if user_choice == computer_choice:
return "It's a tie!"
elif (user_choice == 'rock' and computer_choice == 'scissors') or (user_choice == 'paper' and computer_choice == 'rock') or (user_choice == 'scissors' and computer_choice == 'paper'):
return "You won!"
else:
return "Computer won!"
def play_game(img):
user_probs = classify_image(img)
user_choice = max(user_probs, key=user_probs.get)
computer_choice = random.choice(categories)
winner = determine_winner(user_choice, computer_choice)
computer_image = get_image_files(f'{computer_choice}.jpg')
return f"User's choice: {user_choice}\nComputer's choice: {computer_choice}\n{winner}"#, computer_image
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
examples = ['rock.jpg', 'paper.jpg', 'scissor.jpg']
intf = gr.Interface(fn=play_game, inputs=image, outputs= label, examples = examples)
#intf.blocks[0].block_id = 0 # Unique ID for the image input block
#intf.blocks[1].block_id = 1 # Unique ID for the label output block
intf.launch(inline=False)
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